




import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
_lowerCamelCase      :						List[Any]							=  logging.get_logger("""transformers.models.encodec""")

_lowerCamelCase      :						str							=  {
    """quantizer.vq.layers.*._codebook.inited""": """quantizer.layers.*.codebook.inited""",
    """quantizer.vq.layers.*._codebook.cluster_size""": """quantizer.layers.*.codebook.cluster_size""",
    """quantizer.vq.layers.*._codebook.embed""": """quantizer.layers.*.codebook.embed""",
    """quantizer.vq.layers.*._codebook.embed_avg""": """quantizer.layers.*.codebook.embed_avg""",
}
_lowerCamelCase      :						Union[str, Any]							=  {
    """encoder.model.0.conv.conv""": """encoder.layers.0.conv""",
    """encoder.model.1.block.1.conv.conv""": """encoder.layers.1.block.1.conv""",
    """encoder.model.1.block.3.conv.conv""": """encoder.layers.1.block.3.conv""",
    """encoder.model.1.shortcut.conv.conv""": """encoder.layers.1.shortcut.conv""",
    """encoder.model.3.conv.conv""": """encoder.layers.3.conv""",
    """encoder.model.4.block.1.conv.conv""": """encoder.layers.4.block.1.conv""",
    """encoder.model.4.block.3.conv.conv""": """encoder.layers.4.block.3.conv""",
    """encoder.model.4.shortcut.conv.conv""": """encoder.layers.4.shortcut.conv""",
    """encoder.model.6.conv.conv""": """encoder.layers.6.conv""",
    """encoder.model.7.block.1.conv.conv""": """encoder.layers.7.block.1.conv""",
    """encoder.model.7.block.3.conv.conv""": """encoder.layers.7.block.3.conv""",
    """encoder.model.7.shortcut.conv.conv""": """encoder.layers.7.shortcut.conv""",
    """encoder.model.9.conv.conv""": """encoder.layers.9.conv""",
    """encoder.model.10.block.1.conv.conv""": """encoder.layers.10.block.1.conv""",
    """encoder.model.10.block.3.conv.conv""": """encoder.layers.10.block.3.conv""",
    """encoder.model.10.shortcut.conv.conv""": """encoder.layers.10.shortcut.conv""",
    """encoder.model.12.conv.conv""": """encoder.layers.12.conv""",
    """encoder.model.13.lstm""": """encoder.layers.13.lstm""",
    """encoder.model.15.conv.conv""": """encoder.layers.15.conv""",
}
_lowerCamelCase      :						List[Any]							=  {
    """encoder.model.0.conv.norm""": """encoder.layers.0.norm""",
    """encoder.model.1.block.1.conv.norm""": """encoder.layers.1.block.1.norm""",
    """encoder.model.1.block.3.conv.norm""": """encoder.layers.1.block.3.norm""",
    """encoder.model.1.shortcut.conv.norm""": """encoder.layers.1.shortcut.norm""",
    """encoder.model.3.conv.norm""": """encoder.layers.3.norm""",
    """encoder.model.4.block.1.conv.norm""": """encoder.layers.4.block.1.norm""",
    """encoder.model.4.block.3.conv.norm""": """encoder.layers.4.block.3.norm""",
    """encoder.model.4.shortcut.conv.norm""": """encoder.layers.4.shortcut.norm""",
    """encoder.model.6.conv.norm""": """encoder.layers.6.norm""",
    """encoder.model.7.block.1.conv.norm""": """encoder.layers.7.block.1.norm""",
    """encoder.model.7.block.3.conv.norm""": """encoder.layers.7.block.3.norm""",
    """encoder.model.7.shortcut.conv.norm""": """encoder.layers.7.shortcut.norm""",
    """encoder.model.9.conv.norm""": """encoder.layers.9.norm""",
    """encoder.model.10.block.1.conv.norm""": """encoder.layers.10.block.1.norm""",
    """encoder.model.10.block.3.conv.norm""": """encoder.layers.10.block.3.norm""",
    """encoder.model.10.shortcut.conv.norm""": """encoder.layers.10.shortcut.norm""",
    """encoder.model.12.conv.norm""": """encoder.layers.12.norm""",
    """encoder.model.15.conv.norm""": """encoder.layers.15.norm""",
}
_lowerCamelCase      :						Any							=  {
    """decoder.model.0.conv.conv""": """decoder.layers.0.conv""",
    """decoder.model.1.lstm""": """decoder.layers.1.lstm""",
    """decoder.model.3.convtr.convtr""": """decoder.layers.3.conv""",
    """decoder.model.4.block.1.conv.conv""": """decoder.layers.4.block.1.conv""",
    """decoder.model.4.block.3.conv.conv""": """decoder.layers.4.block.3.conv""",
    """decoder.model.4.shortcut.conv.conv""": """decoder.layers.4.shortcut.conv""",
    """decoder.model.6.convtr.convtr""": """decoder.layers.6.conv""",
    """decoder.model.7.block.1.conv.conv""": """decoder.layers.7.block.1.conv""",
    """decoder.model.7.block.3.conv.conv""": """decoder.layers.7.block.3.conv""",
    """decoder.model.7.shortcut.conv.conv""": """decoder.layers.7.shortcut.conv""",
    """decoder.model.9.convtr.convtr""": """decoder.layers.9.conv""",
    """decoder.model.10.block.1.conv.conv""": """decoder.layers.10.block.1.conv""",
    """decoder.model.10.block.3.conv.conv""": """decoder.layers.10.block.3.conv""",
    """decoder.model.10.shortcut.conv.conv""": """decoder.layers.10.shortcut.conv""",
    """decoder.model.12.convtr.convtr""": """decoder.layers.12.conv""",
    """decoder.model.13.block.1.conv.conv""": """decoder.layers.13.block.1.conv""",
    """decoder.model.13.block.3.conv.conv""": """decoder.layers.13.block.3.conv""",
    """decoder.model.13.shortcut.conv.conv""": """decoder.layers.13.shortcut.conv""",
    """decoder.model.15.conv.conv""": """decoder.layers.15.conv""",
}
_lowerCamelCase      :						Tuple							=  {
    """decoder.model.0.conv.norm""": """decoder.layers.0.norm""",
    """decoder.model.3.convtr.norm""": """decoder.layers.3.norm""",
    """decoder.model.4.block.1.conv.norm""": """decoder.layers.4.block.1.norm""",
    """decoder.model.4.block.3.conv.norm""": """decoder.layers.4.block.3.norm""",
    """decoder.model.4.shortcut.conv.norm""": """decoder.layers.4.shortcut.norm""",
    """decoder.model.6.convtr.norm""": """decoder.layers.6.norm""",
    """decoder.model.7.block.1.conv.norm""": """decoder.layers.7.block.1.norm""",
    """decoder.model.7.block.3.conv.norm""": """decoder.layers.7.block.3.norm""",
    """decoder.model.7.shortcut.conv.norm""": """decoder.layers.7.shortcut.norm""",
    """decoder.model.9.convtr.norm""": """decoder.layers.9.norm""",
    """decoder.model.10.block.1.conv.norm""": """decoder.layers.10.block.1.norm""",
    """decoder.model.10.block.3.conv.norm""": """decoder.layers.10.block.3.norm""",
    """decoder.model.10.shortcut.conv.norm""": """decoder.layers.10.shortcut.norm""",
    """decoder.model.12.convtr.norm""": """decoder.layers.12.norm""",
    """decoder.model.13.block.1.conv.norm""": """decoder.layers.13.block.1.norm""",
    """decoder.model.13.block.3.conv.norm""": """decoder.layers.13.block.3.norm""",
    """decoder.model.13.shortcut.conv.norm""": """decoder.layers.13.shortcut.norm""",
    """decoder.model.15.conv.norm""": """decoder.layers.15.norm""",
}
_lowerCamelCase      :						str							=  {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
_lowerCamelCase      :						Dict							=  {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
_lowerCamelCase      :						Any							=  []
_lowerCamelCase      :						int							=  []







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_					,							lowercase_					,							lowercase_		)  ->			List[str]:



       """simple docstring"""

       for attribute in key.split('''.'''		):
              A__ 					=       getattr(lowercase_					,							lowercase_		)

       if weight_type is not None:
              A__ 					=       getattr(lowercase_					,							lowercase_		).shape
       else:
              A__ 					=       hf_pointer.shape

       if hf_shape != value.shape:
              raise ValueError(
                  f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
                  f""" {value.shape} for {full_name}"""		)

       if weight_type == "weight":
              A__ 					=       value
       elif weight_type == "weight_g":
              A__ 					=       value
       elif weight_type == "weight_v":
              A__ 					=       value
       elif weight_type == "bias":
              A__ 					=       value
       elif weight_type == "running_mean":
              A__ 					=       value
       elif weight_type == "running_var":
              A__ 					=       value
       elif weight_type == "num_batches_tracked":
              A__ 					=       value
       elif weight_type == "weight_ih_l0":
              A__ 					=       value
       elif weight_type == "weight_hh_l0":
              A__ 					=       value
       elif weight_type == "bias_ih_l0":
              A__ 					=       value
       elif weight_type == "bias_hh_l0":
              A__ 					=       value
       elif weight_type == "weight_ih_l1":
              A__ 					=       value
       elif weight_type == "weight_hh_l1":
              A__ 					=       value
       elif weight_type == "bias_ih_l1":
              A__ 					=       value
       elif weight_type == "bias_hh_l1":
              A__ 					=       value
       else:
              A__ 					=       value

       logger.info(f"""{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}."""		)







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_		)  ->			int:



       """simple docstring"""

       for key in ignore_keys:
              if key.endswith('''.*'''		):
                     if name.startswith(key[:-1]		):
                            return True
              elif ".*." in key:
                     A__					,				A__ 					=       key.split('''.*.'''		)
                     if prefix in name and suffix in name:
                            return True
              elif key in name:
                     return True
       return False







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_		)  ->			Union[str, Any]:



       """simple docstring"""

       A__ 					=       []

       if model_name == "encodec_24khz" or "encodec_32khz":
              A__ 					=       MAPPING_24K
       elif model_name == "encodec_48khz":
              A__ 					=       MAPPING_48K
       else:
              raise ValueError(f"""Unsupported model: {model_name}"""		)

       for name, value in orig_dict.items():
              if should_ignore(lowercase_					,							lowercase_		):
                     logger.info(f"""{name} was ignored"""		)
                     continue

              A__ 					=       False
              for key, mapped_key in MAPPING.items():
                     if "*" in key:
                            A__					,				A__ 					=       key.split('''.*.'''		)
                            if prefix in name and suffix in name:
                                   A__ 					=       suffix

                     if key in name:
                            # HACK otherwise .embed gets initialized with .embed_avg too
                            if key.endswith('''embed'''		) and name.endswith('''embed_avg'''		):
                                   continue

                            A__ 					=       True
                            if "*" in mapped_key:
                                   A__ 					=       name.split(lowercase_		)[0].split('''.'''		)[-2]
                                   A__ 					=       mapped_key.replace('''*'''					,							lowercase_		)
                            if "weight_g" in name:
                                   A__ 					=       '''weight_g'''
                            elif "weight_v" in name:
                                   A__ 					=       '''weight_v'''
                            elif "weight_ih_l0" in name:
                                   A__ 					=       '''weight_ih_l0'''
                            elif "weight_hh_l0" in name:
                                   A__ 					=       '''weight_hh_l0'''
                            elif "bias_ih_l0" in name:
                                   A__ 					=       '''bias_ih_l0'''
                            elif "bias_hh_l0" in name:
                                   A__ 					=       '''bias_hh_l0'''
                            elif "weight_ih_l1" in name:
                                   A__ 					=       '''weight_ih_l1'''
                            elif "weight_hh_l1" in name:
                                   A__ 					=       '''weight_hh_l1'''
                            elif "bias_ih_l1" in name:
                                   A__ 					=       '''bias_ih_l1'''
                            elif "bias_hh_l1" in name:
                                   A__ 					=       '''bias_hh_l1'''
                            elif "bias" in name:
                                   A__ 					=       '''bias'''
                            elif "weight" in name:
                                   A__ 					=       '''weight'''
                            elif "running_mean" in name:
                                   A__ 					=       '''running_mean'''
                            elif "running_var" in name:
                                   A__ 					=       '''running_var'''
                            elif "num_batches_tracked" in name:
                                   A__ 					=       '''num_batches_tracked'''
                            else:
                                   A__ 					=       None
                            set_recursively(lowercase_					,							lowercase_					,							lowercase_					,							lowercase_					,							lowercase_		)
                     continue
              if not is_used:
                     unused_weights.append(lowercase_		)

       logger.warning(f"""Unused weights: {unused_weights}"""		)







@torch.no_grad()
def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_					,							lowercase_=None					,							lowercase_=None					,							)  ->			Optional[Any]:



       """simple docstring"""

       if config_path is not None:
              A__ 					=       EncodecConfig.from_pretrained(lowercase_		)
       else:
              A__ 					=       EncodecConfig()

       if model_name == "encodec_24khz":
              pass  # config is already correct
       elif model_name == "encodec_32khz":
              A__ 					=       [8, 5, 4, 4]
              A__ 					=       [2.2]
              A__ 					=       64
              A__ 					=       32_000
              A__ 					=       2_048
              A__ 					=       False
              A__ 					=       False
              A__ 					=       False
       elif model_name == "encodec_48khz":
              A__ 					=       [8, 5, 4, 2]
              A__ 					=       [3.0, 6.0, 12.0, 24.0]
              A__ 					=       48_000
              A__ 					=       2
              A__ 					=       False
              A__ 					=       '''time_group_norm'''
              A__ 					=       True
              A__ 					=       1.0
              A__ 					=       0.01
       else:
              raise ValueError(f"""Unknown model name: {model_name}"""		)

       A__ 					=       EncodecModel(lowercase_		)

       A__ 					=       EncodecFeatureExtractor(
           feature_size=config.audio_channels					,							sampling_rate=config.sampling_rate					,							chunk_length_s=config.chunk_length_s					,							overlap=config.overlap					,							)
       feature_extractor.save_pretrained(lowercase_		)

       A__ 					=       torch.load(lowercase_		)
       if "best_state" in original_checkpoint:
              # we might have a training state saved, in which case discard the yaml results and just retain the weights
              A__ 					=       original_checkpoint['''best_state''']
       recursively_load_weights(lowercase_					,							lowercase_					,							lowercase_		)
       model.save_pretrained(lowercase_		)

       if repo_id:
              print('''Pushing to the hub...'''		)
              feature_extractor.push_to_hub(lowercase_		)
              model.push_to_hub(lowercase_		)


if __name__ == "__main__":
  _lowerCamelCase      :						Optional[int]							=  argparse.ArgumentParser()
  parser.add_argument(
      """--model""",
      default="""encodec_24khz""",
      type=str,
      help="""The model to convert. Should be one of 'encodec_24khz', 'encodec_32khz', 'encodec_48khz'.""",
  )
  parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
  parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
  parser.add_argument(
      """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
  )
  parser.add_argument(
      """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
  )

  _lowerCamelCase      :						Union[str, Any]							=  parser.parse_args()
  convert_checkpoint(
      args.model,
      args.checkpoint_path,
      args.pytorch_dump_folder_path,
      args.config_path,
      args.push_to_hub,
  )







def  lowerCAmelCase_	(lowerCAmelCase__:		int  ):





			"""simple docstring"""


			if num <= 0:
						raise ValueError("""Input must be a positive integer"""  )

			UpperCAmelCase_:		Tuple							      =     [True] * (num + 1)

			UpperCAmelCase_:		Tuple							      =     2
			while p * p <= num:
						if primes[p]:
									for i in range(p * p		,						num + 1		,						lowerCAmelCase__  ):
												UpperCAmelCase_:		Optional[Any]							      =     False
						p += 1

			return [prime for prime in range(2		,						num + 1  ) if primes[prime]]


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	a      :  Optional[Any]			     =			int(input('Enter a positive integer: ').strip())
	print(prime_sieve_eratosthenes(user_num))






